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Accurate discharge records are essential for flood frequency analyses, hydraulic model calibration and flood forecasting. Discharge records are often obtained via a transformation of water levels to discharges using a rating curve. For accurate rating curves, a physical basis is important, particularly in the extrapolation domain towards extreme discharges. In this study, physical processes and constraints are incorporated in a rating curve model: water balance closure at a bifurcation and bed level degradation. The aim is to assess the effect of incorporating these physical processes and constraints for rating curves at two bifurcations of the Rhine river in the Netherlands. Intermittent gaugings are available for a 31 year period at these bifurcations. Bayesian inference and Markov Chain Monte Carlo sampling is used to estimate the posterior distributions of the rating curves. If rating curves are constructed independently, they show a large water balance error at bifurcations of up to 10%. Incorporating bed level degradation is required for accurate rating curves as it reduces the residual errors by up to 50%. If explicitly accounting for water balance closure, the water balance error can be reduced to 1%, while residual errors remain equally small. As water balance closure is a physical constraint at a river bifurcation, the rating curves that account for water balance closure are expected to be more physically realistic. Therefore, it is recommended to specifically gauge at river bifurcations and confluences, such that the constraint of water balance closure can be used to improve the accuracy of rating curves and discharge records.

Accurate rating curves are essential for a wide range of river management pur­ poses, particularly as a basis for flood risk management. In our research, we investigate rating curve uncertainties as related to flow measurement errors. We consider the three largest Dutch river Rhine branches (Bovenrijn, Waal and Pannerdensch Kanaal) and the bifurcation point Pan­ nerdense Kop. Comparing the official rating curves for these river channels shows that the water balance is not closing (up to 5% deviation), and this gives a direct indication of the uncertainty in the rating curves. We quantify rating curve uncertainty using Bayesian inference and Markov chain Monte Carlo simulations, as based on homogenous measurement data sets. Next, we show how water balance considerations can influence the uncertainty of rating curves. Finally, we dis­ cuss implications for current flood risk norms in the Netherlands and implications for future hydrometric campaigns.

The IAHR Working Group on Compound Channels started its activities in 2007, under the auspices of the Fluvial Hydraulic Committee. Its main purpose is to foster collaboration and information sharing amongst researchers involved in compound channel modeling. As part of its activities, this group recently conducted a first benchmarking exercise, devoted to uniform flow in a prismatic compound channel. The paper gives the benchmark and summarizes the results obtained with various models. 9p DHA/NRE

Accurate predictions of water levels play an important role in the management of flood safety. Nowadays, it has become common practice to use multi-dimensional numerical hydrodynamic models for such purposes. Currently, two model types are the standard tools in the Netherlands, namely WAQUA/TRIWAQ (Rijkswaterstaat, 2012) and Delft3D (Deltares, 2014). WAQUA and Delft3D are both based on a structured curvilinear grid, which can follow large-scale topographical changes and uses similar grid resolution throughout the entire computational domain. Drawbacks of the structured curvilinear grid approach are that elevation jumps in the river's topography may lead to unrealistic staircase representations in the model, and the inner bends of meandering rivers gridlines may become focussed to unnecessarily small grid cells (Kernkamp et al., 2011). To improve on these issues, Deltares is developing the unstructured-grid-based hydrodynamic model Flexible Mesh (also referred to as “D-Flow-FM”). The unstructured grid approach enables the user to use a spatially variable grid resolution. By combining curvilinear grid cells with triangular grid cells, the modeller can increase grid resolution on the locations where, because of local topographical variations, it is most desired. In this study, modelling results of Flexible Mesh and WAQUA are presented for a selected river reach near Afferden-Deest and benefits of local grid refinements in Flexible Mesh are demonstrated.

Land cover maps provide essential input data for various hydromorphological and ecological models, but the effect of land cover classification errors on these models has not been quantified systematically. This paper presents the uncertainty in hydromorphological and ecological model output for a large lowland river depending on the classification accuracy (CA) of a land cover map. Using four different models, we quantified the uncertainty for the three distributaries of the Rhine River in The Netherlands with respect to: (1) hydrodynamics (WAQUA model), (2) annual average suspended sediment deposition (SEDIFLUX model), (3) ecotoxicological hazards of contaminated sediment for a bird of prey, and (4) floodplain importance for desired habitat types and species (BIO-SAFE model). We carried out two Monte Carlo (n = 15) analyses: one at a 69% land cover CA, the other at 95% CA. Subsequently we ran all four models with the 30 realizations as input. The error in the current land cover map gave an uncertainty in design water levels of up to 19 cm. Overbank sediment deposition varied up to 100% in the area bordering the main channel, but when aggregated to the whole study area, the variation in sediment trapping efficiency was negligible. The ecotoxicological hazards, represented by the fraction of Little Owl habitat with potential cadmium exposure levels exceeding a corresponding toxicity threshold of 148 @mg d^-^1, varied between 54 and 60%, aggregated over the distributaries. The 68% confidence interval of floodplain importance for protected and endangered species varied between 10 and 15%. Increasing the classification accuracy to 95% significantly lowered the uncertainty of all models applied. Compared to landscaping measures, the effects due to the uncertainty in the land cover map are of the same order of magnitude. Given high financial costs of these landscaping measures, increasing the classification accuracy of land cover maps is a prerequisite for improving the assessment of the efficiency of landscaping measures.

Landcover maps provide essential input data for a sequence of models used to quantify the hydrodynamics and ecology of lowland rivers and their floodplains. Hydrodynamic models provide estimates of peak water levels and sediment deposition while ecological models characterize habitat suitability, biodiversity (Lenders et al., 2001; Schipper et al., 2008) and ecosystem services (Nelson et al., 2009). Such models are routinely used in the environmental impact assessment of landscaping measures that aim to reduce the flood risk and improve the ecological quality of the river area (Van Stokkom et al., 2005; Samuels et al., 2006). In general, however, the impact of landcover classification errors on hydrodynamic and ecological model output has hardly been quantified. Our main objective is to assess the ncertainty in biogeomorphological modeling of a lowland river depending on the classification accuracy of landcover maps.